We will first begin by loading in the packages we intend to use.
Then, importing the data using a URL directly from the source, ensures we will capture updates to the data as they come in, whenever this is run again.
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(fig.width = 12, fig.height = 8)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.1 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.2 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(lubridate)
library(ggmap)
## ℹ Google's Terms of Service: <https://mapsplatform.google.com>
## ℹ Please cite ggmap if you use it! Use `citation("ggmap")` for details.
library(ggplot2)
import_url <- read.csv("https://data.cityofnewyork.us/api/views/833y-fsy8/rows.csv?accessType=DOWNLOAD")
Let’s take a look at the dimensions of this imported data.frame, as well as the variable types of each column, and summary.
dim(import_url)
## [1] 25596 19
str(import_url)
## 'data.frame': 25596 obs. of 19 variables:
## $ INCIDENT_KEY : int 236168668 231008085 230717903 237712309 224465521 228252164 226950018 237710987 224701998 225295736 ...
## $ OCCUR_DATE : chr "11/11/2021" "07/16/2021" "07/11/2021" "12/11/2021" ...
## $ OCCUR_TIME : chr "15:04:00" "22:05:00" "01:09:00" "13:42:00" ...
## $ BORO : chr "BROOKLYN" "BROOKLYN" "BROOKLYN" "BROOKLYN" ...
## $ PRECINCT : int 79 72 79 81 113 113 42 52 34 75 ...
## $ JURISDICTION_CODE : int 0 0 0 0 0 0 0 0 0 0 ...
## $ LOCATION_DESC : chr "" "" "" "" ...
## $ STATISTICAL_MURDER_FLAG: chr "false" "false" "false" "false" ...
## $ PERP_AGE_GROUP : chr "" "45-64" "<18" "" ...
## $ PERP_SEX : chr "" "M" "M" "" ...
## $ PERP_RACE : chr "" "ASIAN / PACIFIC ISLANDER" "BLACK" "" ...
## $ VIC_AGE_GROUP : chr "18-24" "25-44" "25-44" "25-44" ...
## $ VIC_SEX : chr "M" "M" "M" "M" ...
## $ VIC_RACE : chr "BLACK" "ASIAN / PACIFIC ISLANDER" "BLACK" "BLACK" ...
## $ X_COORD_CD : num 996313 981845 996546 1001139 1050710 ...
## $ Y_COORD_CD : num 187499 171118 187436 192775 184826 ...
## $ Latitude : num 40.7 40.6 40.7 40.7 40.7 ...
## $ Longitude : num -74 -74 -74 -73.9 -73.8 ...
## $ Lon_Lat : chr "POINT (-73.95650899099996 40.68131820000008)" "POINT (-74.00866668999998 40.63636384100005)" "POINT (-73.95566903799994 40.68114495900005)" "POINT (-73.939095905 40.69579171600003)" ...
summary(import_url)
## INCIDENT_KEY OCCUR_DATE OCCUR_TIME BORO
## Min. : 9953245 Length:25596 Length:25596 Length:25596
## 1st Qu.: 61593633 Class :character Class :character Class :character
## Median : 86437258 Mode :character Mode :character Mode :character
## Mean :112382648
## 3rd Qu.:166660833
## Max. :238490103
##
## PRECINCT JURISDICTION_CODE LOCATION_DESC STATISTICAL_MURDER_FLAG
## Min. : 1.00 Min. :0.0000 Length:25596 Length:25596
## 1st Qu.: 44.00 1st Qu.:0.0000 Class :character Class :character
## Median : 69.00 Median :0.0000 Mode :character Mode :character
## Mean : 65.87 Mean :0.3316
## 3rd Qu.: 81.00 3rd Qu.:0.0000
## Max. :123.00 Max. :2.0000
## NA's :2
## PERP_AGE_GROUP PERP_SEX PERP_RACE VIC_AGE_GROUP
## Length:25596 Length:25596 Length:25596 Length:25596
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## VIC_SEX VIC_RACE X_COORD_CD Y_COORD_CD
## Length:25596 Length:25596 Min. : 914928 Min. :125757
## Class :character Class :character 1st Qu.:1000011 1st Qu.:182782
## Mode :character Mode :character Median :1007715 Median :194038
## Mean :1009455 Mean :207894
## 3rd Qu.:1016838 3rd Qu.:239429
## Max. :1066815 Max. :271128
##
## Latitude Longitude Lon_Lat
## Min. :40.51 Min. :-74.25 Length:25596
## 1st Qu.:40.67 1st Qu.:-73.94 Class :character
## Median :40.70 Median :-73.92 Mode :character
## Mean :40.74 Mean :-73.91
## 3rd Qu.:40.82 3rd Qu.:-73.88
## Max. :40.91 Max. :-73.70
##
Looks like we have 19 columns (features) and 25596
rows (data points).
First, let’s remove any features that we won’t be needing for our
analysis.
JURISDICTION_CODE is pretty broad for localizing
shooting incidents so we will end up using BORO instead
which will give more insight to our analysis.X_COORD_CD, Y_COORD_CD, and
Lon_Lat are all redundant.Also, let’s rename a few of these for more readability.
import_url <- select(import_url, -JURISDICTION_CODE, -X_COORD_CD, -Y_COORD_CD, -Lon_Lat)
import_url <- import_url %>%
rename(c('DATE' = 'OCCUR_DATE', 'TIME' = 'OCCUR_TIME','BOROUGH' = 'BORO',
'LOCATION' = 'LOCATION_DESC', 'MURDER_FLAG' = 'STATISTICAL_MURDER_FLAG',
'VICTIM_AGE' = 'VIC_AGE_GROUP', 'VICTIM_SEX' = 'VIC_SEX', 'VICTIM_RACE' = 'VIC_RACE',
'LATITUDE' = 'Latitude', 'LONGITUDE' = 'Longitude'))
head(import_url)
## INCIDENT_KEY DATE TIME BOROUGH PRECINCT LOCATION MURDER_FLAG
## 1 236168668 11/11/2021 15:04:00 BROOKLYN 79 false
## 2 231008085 07/16/2021 22:05:00 BROOKLYN 72 false
## 3 230717903 07/11/2021 01:09:00 BROOKLYN 79 false
## 4 237712309 12/11/2021 13:42:00 BROOKLYN 81 false
## 5 224465521 02/16/2021 20:00:00 QUEENS 113 false
## 6 228252164 05/15/2021 04:13:00 QUEENS 113 true
## PERP_AGE_GROUP PERP_SEX PERP_RACE VICTIM_AGE VICTIM_SEX
## 1 18-24 M
## 2 45-64 M ASIAN / PACIFIC ISLANDER 25-44 M
## 3 <18 M BLACK 25-44 M
## 4 25-44 M
## 5 25-44 M
## 6 25-44 M
## VICTIM_RACE LATITUDE LONGITUDE
## 1 BLACK 40.68132 -73.95651
## 2 ASIAN / PACIFIC ISLANDER 40.63636 -74.00867
## 3 BLACK 40.68114 -73.95567
## 4 BLACK 40.69579 -73.93910
## 5 BLACK 40.67374 -73.76041
## 6 BLACK 40.70618 -73.75806
Next, we will check if there are any missing or duplicated data
points, focusing only on the INCIDENT_KEY feature for now.
This feature will be the most important for identifying any duplicate
entries as they should all be unique.
# Check for any NA or Null values
any(is.na(import_url$INCIDENT_KEY)) | any(is.null(import_url$INCIDENT_KEY))
## [1] FALSE
# Check for duplicates
length(unique(import_url$INCIDENT_KEY))
## [1] 20126
length(import_url$INCIDENT_KEY)
## [1] 25596
Subtracting the results here shows that there are 5470 duplicate data points. Let’s take a look to make sure these aren’t false positives.
# Sort duplicates to see what they look like, but does not change dataframe
head(filter(import_url, duplicated(import_url$INCIDENT_KEY)))
## INCIDENT_KEY DATE TIME BOROUGH PRECINCT LOCATION
## 1 229643172 06/16/2021 23:22:00 BRONX 52
## 2 236363733 11/16/2021 22:39:00 MANHATTAN 14
## 3 226542152 04/05/2021 22:10:00 BRONX 44 MULTI DWELL - PUBLIC HOUS
## 4 227647476 05/02/2021 18:18:00 MANHATTAN 23 MULTI DWELL - PUBLIC HOUS
## 5 232496781 08/19/2021 20:32:00 BROOKLYN 77
## 6 232390408 08/17/2021 22:20:00 BROOKLYN 73 GROCERY/BODEGA
## MURDER_FLAG PERP_AGE_GROUP PERP_SEX PERP_RACE VICTIM_AGE VICTIM_SEX
## 1 false 18-24 M WHITE HISPANIC 25-44 F
## 2 false <18 M BLACK 25-44 M
## 3 false 45-64 M
## 4 false 18-24 M WHITE HISPANIC 25-44 M
## 5 false 45-64 M BLACK 45-64 F
## 6 false 18-24 M
## VICTIM_RACE LATITUDE LONGITUDE
## 1 BLACK 40.86414 -73.89131
## 2 BLACK 40.75165 -73.98434
## 3 BLACK 40.83750 -73.92785
## 4 BLACK 40.78694 -73.94357
## 5 BLACK 40.67036 -73.92680
## 6 BLACK 40.66835 -73.90652
# Check a few entries
arrange(filter(import_url, INCIDENT_KEY == 227647476 | INCIDENT_KEY == 232390408), INCIDENT_KEY)
## INCIDENT_KEY DATE TIME BOROUGH PRECINCT LOCATION
## 1 227647476 05/02/2021 18:18:00 MANHATTAN 23 MULTI DWELL - PUBLIC HOUS
## 2 227647476 05/02/2021 18:18:00 MANHATTAN 23 MULTI DWELL - PUBLIC HOUS
## 3 227647476 05/02/2021 18:18:00 MANHATTAN 23 MULTI DWELL - PUBLIC HOUS
## 4 232390408 08/17/2021 22:20:00 BROOKLYN 73 GROCERY/BODEGA
## 5 232390408 08/17/2021 22:20:00 BROOKLYN 73 GROCERY/BODEGA
## MURDER_FLAG PERP_AGE_GROUP PERP_SEX PERP_RACE VICTIM_AGE VICTIM_SEX
## 1 false <18 M BLACK 25-44 M
## 2 false 18-24 M WHITE HISPANIC 25-44 M
## 3 false 18-24 M BLACK 25-44 M
## 4 false 25-44 M
## 5 false 18-24 M
## VICTIM_RACE LATITUDE LONGITUDE
## 1 BLACK 40.78694 -73.94357
## 2 BLACK 40.78694 -73.94357
## 3 BLACK 40.78694 -73.94357
## 4 BLACK 40.66835 -73.90652
## 5 BLACK 40.66835 -73.90652
# Remove duplicates
import_url <- filter(import_url, !duplicated(import_url$INCIDENT_KEY))
# Check work
sum(duplicated(import_url$INCIDENT_KEY))
## [1] 0
For better analysis we should change the class type of a few of these features to make them easier to work with.
# Character to Date and Period
import_url <- import_url %>% mutate(DATE = mdy(DATE)) %>%
mutate(TIME = hms(TIME))
# Character to Factors - changes all character columns to factor
import_url <- import_url %>% mutate(across(where(is.character), as.factor))
str(import_url)
## 'data.frame': 20126 obs. of 15 variables:
## $ INCIDENT_KEY : int 236168668 231008085 230717903 237712309 224465521 228252164 226950018 237710987 224701998 225295736 ...
## $ DATE : Date, format: "2021-11-11" "2021-07-16" ...
## $ TIME :Formal class 'Period' [package "lubridate"] with 6 slots
## .. ..@ .Data : num 0 0 0 0 0 0 0 0 0 0 ...
## .. ..@ year : num 0 0 0 0 0 0 0 0 0 0 ...
## .. ..@ month : num 0 0 0 0 0 0 0 0 0 0 ...
## .. ..@ day : num 0 0 0 0 0 0 0 0 0 0 ...
## .. ..@ hour : num 15 22 1 13 20 4 21 19 0 6 ...
## .. ..@ minute: num 4 5 9 42 0 13 8 30 18 15 ...
## $ BOROUGH : Factor w/ 5 levels "BRONX","BROOKLYN",..: 2 2 2 2 4 4 1 1 3 2 ...
## $ PRECINCT : int 79 72 79 81 113 113 42 52 34 75 ...
## $ LOCATION : Factor w/ 40 levels "","ATM","BANK",..: 1 1 1 1 1 1 10 1 1 1 ...
## $ MURDER_FLAG : Factor w/ 2 levels "false","true": 1 1 1 1 1 2 2 1 1 2 ...
## $ PERP_AGE_GROUP: Factor w/ 10 levels "","<18","1020",..: 1 7 2 1 1 1 1 1 1 6 ...
## $ PERP_SEX : Factor w/ 4 levels "","F","M","U": 1 3 3 1 1 1 1 1 1 3 ...
## $ PERP_RACE : Factor w/ 8 levels "","AMERICAN INDIAN/ALASKAN NATIVE",..: 1 3 4 1 1 1 1 1 1 5 ...
## $ VICTIM_AGE : Factor w/ 6 levels "<18","18-24",..: 2 3 3 3 3 3 2 3 3 3 ...
## $ VICTIM_SEX : Factor w/ 3 levels "F","M","U": 2 2 2 2 2 2 2 2 2 2 ...
## $ VICTIM_RACE : Factor w/ 7 levels "AMERICAN INDIAN/ALASKAN NATIVE",..: 3 2 3 3 3 3 3 3 4 7 ...
## $ LATITUDE : num 40.7 40.6 40.7 40.7 40.7 ...
## $ LONGITUDE : num -74 -74 -74 -73.9 -73.8 ...
We will continue to look at the features and see if any of these blank entries will cause trouble during the analysis. Also, we’ll look to see if there are any duplicate categorical factors in the rest of the features.
# Create a table of each column to check factor levels
for(i in 1:length(import_url)){
ifelse(is.factor(import_url[ ,i]), print(table(import_url[ ,i, drop = FALSE])), next)
}
## BOROUGH
## BRONX BROOKLYN MANHATTAN QUEENS STATEN ISLAND
## 5611 8357 2536 3031 591
## LOCATION
## ATM BANK
## 11964 1 2
## BAR/NIGHT CLUB BEAUTY/NAIL SALON CANDY STORE
## 400 77 6
## CHAIN STORE CHECK CASH CLOTHING BOUTIQUE
## 5 1 11
## COMMERCIAL BLDG DEPT STORE DOCTOR/DENTIST
## 194 5 1
## DRUG STORE DRY CLEANER/LAUNDRY FACTORY/WAREHOUSE
## 7 24 5
## FAST FOOD GAS STATION GROCERY/BODEGA
## 77 50 468
## GYM/FITNESS FACILITY HOSPITAL HOTEL/MOTEL
## 3 37 25
## JEWELRY STORE LIQUOR STORE LOAN COMPANY
## 9 24 1
## MULTI DWELL - APT BUILD MULTI DWELL - PUBLIC HOUS NONE
## 2007 3664 140
## PHOTO/COPY STORE PVT HOUSE RESTAURANT/DINER
## 1 641 153
## SCHOOL SHOE STORE SMALL MERCHANT
## 1 4 18
## SOCIAL CLUB/POLICY LOCATI STORAGE FACILITY STORE UNCLASSIFIED
## 43 1 28
## SUPERMARKET TELECOMM. STORE VARIETY STORE
## 14 2 10
## VIDEO STORE
## 2
## MURDER_FLAG
## false true
## 16604 3522
## PERP_AGE_GROUP
## <18 1020 18-24 224 25-44 45-64 65+ 940 UNKNOWN
## 8120 993 1 4086 1 3769 374 44 1 2737
## PERP_SEX
## F M U
## 8090 212 10469 1355
## PERP_RACE
## AMERICAN INDIAN/ALASKAN NATIVE
## 8090 2
## ASIAN / PACIFIC ISLANDER BLACK
## 88 7871
## BLACK HISPANIC UNKNOWN
## 803 1627
## WHITE WHITE HISPANIC
## 213 1432
## VICTIM_AGE
## <18 18-24 25-44 45-64 65+ UNKNOWN
## 1984 7585 9122 1273 125 37
## VICTIM_SEX
## F M U
## 1522 18598 6
## VICTIM_RACE
## AMERICAN INDIAN/ALASKAN NATIVE ASIAN / PACIFIC ISLANDER
## 8 260
## BLACK BLACK HISPANIC
## 14742 1854
## UNKNOWN WHITE
## 50 517
## WHITE HISPANIC
## 2695
Here we’re going to visualize the location of each shooting incident
using the coordinates given in the dataset. First, we can use the
minimum and maximum values of the longitudes and latitudes to find the
map’s bounding box (edges). Then, use ggmap() to generate a
map centered around these coordinates. Then, we can use
geom_point() and stat_density2d_filled() to
superimpose our data on the map using the same coordinate system we
generated.
# Initialize the bounding box that will contain the map coordinates.
map_bounds <- c(left = min(import_url$LONGITUDE),
bottom = min(import_url$LATITUDE),
right = max(import_url$LONGITUDE),
top = max(import_url$LATITUDE))
# Initialize the scatter plot of the incident coordinates
# Note, there are better maps out there but most require a private google API key,
# which wouldn't work for this public project.
incident_map_point <- ggmap(get_stamenmap(map_bounds, maptype = 'terrain', zoom = 11)) +
geom_point(data = import_url,
aes(x = LONGITUDE, y = LATITUDE),
color = 'darkred',
size = 0.25,
alpha = 0.2) +
ggtitle('Point Plot of NYPD Shooting Incident Reporting 2006 - 2021\n Source:<https://catalog.data.gov/dataset/nypd-shooting-incident-data-historic>') +
labs(x = 'LONGITUDE', y = 'LATITUDE')
## ℹ Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.
# Display Point Map
incident_map_point
# Initialize density map to better visualize regions with frequent incidents.
incident_map_density <- ggmap(get_stamenmap(map_bounds, maptype = 'terrain', zoom = 11)) +
stat_density2d_filled(data = import_url, contour_var = 'density',
aes(x = LONGITUDE, y = LATITUDE, fill = after_stat(level)),
bins = 20,
geom = 'polygon',
alpha = 0.8) +
geom_density_2d(data = import_url,
aes(x = LONGITUDE, y = LATITUDE),
bins = 20,
alpha = 0.2,
color = "white") +
guides(fill = guide_legend(title = "Density")) +
ggtitle('Density Plot of NYPD Shooting Incident Reporting 2006 - 2021\n Source:<https://catalog.data.gov/dataset/nypd-shooting-incident-data-historic>') +
labs(x = 'LONGITUDE', y = 'LATITUDE')
## ℹ Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.
# Display Density Map
incident_map_density